August 2020
Vol 6, Issue 34

About The Cover

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ONLINE COVER Urban theory models how cities form, deriving their aggregate properties as functions of variables such as population size. While classical modeling approaches have generated predictions consistent with many observed properties in cities, they do not resolve problems of statistics and growth. Bettencourt et al. note that these fundamental problems are linked and must be analyzed together. To build on urban theory, the researchers use 50 years of data on the evolution of U.S. metropolitan areas, applying stochastic differential equations and control theory to build a general statistical dynamics of cities across scales, from single agents to entire urban systems. Bettencourt et al. propose that stochastic growth processes—randomly determined patterns that cannot be precisely predicted—contribute to a common foundation that can be used to model cities across scales as they grow. The findings demonstrate how spatial equilibrium is consistent with observed exponential growth in cities, which was an assumption in previous models, and shows how scientists can derive large-scale statistical behavior for cities based on choices at the agents' level. The study also reveals how issues of wealth and inequality have compounded in the U.S. over time. [CREDIT: ART WAGER/ISTOCK]